A piece of tape cost Denny Hamlin his championship. The same problem that blew up his engine is now one of the biggest profit opportunities in AI.
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Editor’s note: Joe Austin He spent four decades on Wall Street – as a research analyst in the technology sector, a senior portfolio manager overseeing more than $10 billion in assets, and a hedging analyst for a $5 billion hedge fund. Back then, he learned that the biggest profits in any major technology wave rarely go to the most obvious players.
In today’s article, Joe uses a memorable moment from NASCAR to explain why it’s overlooked more than most AI shares Which companies may be most valuable — and why the “behind-the-scenes” companies that make AI physically possible deserve a serious look.
Joe recently partnered with the Wall Street legend for 60 years Mark Chaikin To launch the first tool of its kind powered by artificial intelligence. It’s the first AI-powered product Chaikin Analytics has ever built, and it’s specifically designed to find stocks like the ones Joe describes below, before Wall Street took hold.
Just yesterday, Joe and Mark held a special presentation detailing the tool. You can catch a replay of that here.
Now, here’s Joe…
Denny Hamlin was ready to win his first NASCAR championship. But it all came crashing down because of a piece of tape.
Hamlin was considered one of the best drivers of his generation, but the title always eluded him.
In 2019, he was NASCAR’s comeback kid. He actually won six times that season, including the famous Daytona 500. And it all came down to a cold November night at Homestead-Miami Speedway.
The day started well. Qualifying was canceled due to inclement weather, giving Hamlin the top spot based on his points. In the first two stages, he ranked fifth. By the third stage, things were looking up – he was running second, posting faster lap times than teammate Kyle Busch, who led the race.
Then, with 58 laps to go, crew chief Chris Gabhart made a fateful decision. He brought Hamlin to an early pit stop and asked the crew to slap a large piece of black tape on the front grille.
The idea was sound in theory. In NASCAR, crews routinely hook up grids to gain a competitive advantage. Normally, air enters the grille and bounces around the engine, creating drag. The tape restricts airflow, forcing it to flow smoothly over the car instead. More speed, more downforce, better grip.
But the tape also restricts cooling.
NASCAR engines operate at about 290 degrees Fahrenheit — about 90 degrees hotter than a typical road car. The margin of error is very small. In Hamlin’s case, the tape backfired immediately. The temperature gauges have reached the maximum. Steam started coming out of the engine. Engine failure seemed imminent.
Gabhart had to call Hamlin back to the pits after only 12 laps. He finished 10th – last among the four championship contenders. Kyle Busch won the race and the title.
Excessive heat isn’t just a NASCAR problem. Data centers using AI chips face exactly the same dilemma.
For investors, it represents one of the most overlooked opportunities of the entire AI boom.
Find companies “behind the scenes”.
AI chips need huge amounts of power to train models and run calculations. More power means more heat. If you can’t cool the chips fast enough, performance will suffer or the device will fail altogether.
Inside the data center are long rows of computer racks, which are tall cabinets stacked with servers. A typical AI data center contains hundreds or even thousands of them. Nvidia company (NVDA) The next-generation Vera Rubin chip uses 120 to 130 kilowatts per rack. This is the annual electricity consumption of about 100 American homes – per shelf.
Larger versions of the Rubin chip will use five times that amount of power.
This creates an inevitable physical problem. More power means more heat, in an almost one-on-one relationship. Delivering this much electricity requires a complete rethink of how power is transported through a building.
Think of it like water through a hose. You can deliver the same volume using high pressure through a small hose or low pressure through a huge hose. Electrical energy works the same way – high voltage with low current, or low voltage with high current. High current causes cables to dangerously overheat. Conventional energy systems cannot handle it.
Engineers solved this problem by raising the voltage. The industry has switched to 800 volt power systems, which provide the same power with much lower current and much less heat.
But 800-volt operation requires power chips made of completely different materials. Materials that only a few companies know how to produce.
This is the bigger point about investing in a big trend like AI.
The AI technology itself is receiving media attention. nvidia, Microsoft Corporation (MSFT), Palantir Technologies Inc. (Belter): Everyone knows those names.
But an entirely different set of companies — those that make the components, materials, and systems that allow AI to actually work — are just as important. And much less was chosen.
No matter which big-name AI company builds the next data center, the companies running it behind the scenes will get paid. The question is whether you own any of them.
A tool designed specifically for this purpose
I spent 40 years on Wall Street learning to look one step beyond the obvious story. The Internet boom made millionaires out of people who bought Cisco Systems Inc. (cisco) and Intel Corporation (Intech)And not only Amazon.com Inc. (Amzn) and ebay company (eBay). The shale revolution has affected investors in fracking equipment and pipeline infrastructure, not just oil producers.
The AI boom is set up in the same way. The challenge – as always – is finding the right stocks “behind the scenes” before the crowd does.
That’s exactly what Mark Chaikin And I built Time machine to do.
Time Machine is Chaikin Analytics’ first AI-powered platform, and we revealed it for the first time yesterday during a special free stream (You can watch the replay here). It works by scanning decades of market history to find stocks today whose fundamental and technical fingerprints match the early profiles of proven winners in multiple campaigns — stocks like Nvidia, Amazon, Meta Platforms Inc. (dead)right before their biggest move.
In the backtest, stocks that went on emerged with gains of 995%, 1,406%, and 3,804% — all while the “underlying” stocks they were matched with produced much more modest returns.
AI companies — power chip makers, cooling system specialists, infrastructure suppliers — are exactly the kind of stocks Time Machine was designed to create.
This is the first time Chaikin Analytics has offered an AI-powered product.
Click here to watch the newly released special broadcast and learn more about Time Machine.
good investment,
Joe Austin
Senior Analyst, Chaiken Analytics
